Stanford University
Computational Biologist & Project Manager in Genomics (Biostatistician 2)
Stanford University, Stanford, California, United States, 94305
Overview
Computational Biologist & Project Manager in Genomics (Biostatistician 2) — School of Medicine, Stanford, California, United States. Information Analytics. Requisition #107087. The Engreitz and Kundaje Labs are seeking a Computational Biologist / Project Manager (Biostatistician 2) to join the Department of Genetics to map the regulatory wiring of the human genome to discover genetic mechanisms of disease. The position is open, and a successful candidate could join immediately.
Lab and Project Context Lab overview: DNA regulatory elements in the human genome harbor thousands of genetic risk variants for common and rare diseases and could reveal targets for therapeutics that aim to precisely tune cellular functions. The goal is to map the regulatory wiring that connects millions of elements with genes across many cell types. The Engreitz and Kundaje Labs have developed new experimental approaches and computational methods to enable this at scale, with prior work including Fulco et al. Nature Genetics 2019, Schnitzler & Kang et al. Nature 2024, Avsec et al. Nature Genetics 2021, Pampari et al. bioRxiv 2024. We invent tools combining single-cell genomics, CRISPR perturbations, and machine learning to assemble regulatory maps of the genome and uncover mechanisms of complex diseases. For more information, see https://www.engreitzlab.org and https://kundajelab.github.io/.
Project overview: Develop and apply computational models to interpret the function of noncoding variants or protein-coding genes across many human cell types. We lead collaborative projects in two NIH-funded consortia: MorPhiC (https://morphic.bio) and IGVF (https://www.igvf.org). MorPhiC characterizes gene functions through CRISPR perturbations and predictive modeling (Adli et al. Nature 2025). IGVF characterizes the impact of genomic variation on function by combining single-cell mapping, genomic perturbations, and predictive models (IGVF Consortium, Nature 2024). The role involves improving predictive models of variants, enhancers, and genes and applying them to large single-cell and CRISPR datasets to create a comprehensive catalog of regulatory wiring.
Who We Are Looking For We seek creative and passionate people at any career stage, including computational biologists, bioinformaticians, and software engineers. Candidates will train to lead and design team science computational projects that push genomic technology boundaries and reveal functions of genetic elements tied to human diseases. Our laboratories are co-located in the Department of Genetics and Biomedical Innovations Building at Stanford University. The department is a dynamic, interdisciplinary workplace offering access to cutting-edge technologies and a culture that values diversity of backgrounds and approaches.
Ideal Candidate The ideal candidate should have expertise in bioinformatics and computational biology workflows; statistical methods in data analysis with applications to high-throughput sequencing or other biological assays; fundamentals of software engineering; strong knowledge of molecular biology and genomics; fluency in Unix and standard programming/data analysis languages (Python, R, or equivalent); interest in mentoring and training other lab members in computational biology and statistics; excellent communication, organization, and time management skills; and creativity and motivation.
Responsibilities
Apply state-of-the-art machine learning models to large datasets, including single-cell and Perturb-seq datasets
Interpret model performance and results
Develop standards and pipelines to expand analyses across datasets
Interface with collaborators at Stanford and collaborating labs to design and produce methods and data analysis products
Track and manage contributions by lab members to consortium activities
Design and implement generalizable algorithms and tools for analysis of biological data, including high-throughput functional genomics assays
Evaluate and recommend new emerging technologies, approaches, and problems
Create scientifically rigorous visualizations, communications, and presentations of results
Contribute to generation of protocols, publications, and intellectual property
Maintain and organize computational infrastructure and resources
Note: Other duties may also be assigned.
Qualifications (Desired)
Required: M.S. or Ph.D. in computational biology, genetics, computer science, statistics, math, molecular biology, or related field, or equivalent practical experience. Talented applicants of all levels are encouraged to apply.
Demonstrated expertise in statistical methods in data analysis, preferably with applications to high-throughput sequencing or other biological assays
Experience with data analysis and management, workflow management
Fluency in Unix, standard bioinformatics tools (Python, R, or equivalent), and a programming language (C/C++, Java)
Strong knowledge of molecular biology and functional genomics
Ability to mentor and train other lab members in computational biology and statistics
Excellent communication, organization, and time management skills
Creative, organized, motivated, team player
A passion for science and a sense of urgency to find new medicines to benefit patients
Education & Experience (Required) Master's degree in biostatistics, statistics, or related field and at least 3 years of experience.
Knowledge, Skills and Abilities (Required)
Proficient in at least two of R, SAS, SPSS, or STATA
Skills in descriptive analysis, modeling of data, and graphic interfaces
Outstanding ability to communicate technical information to both technical and non-technical audiences
Demonstrated excellence in at least one area of expertise (e.g., coordinating studies; statistical methodology such as missing data, survival analysis, statistical genetics, or informatics; statistical computing; database design; graphical techniques)
Certifications & Licenses None
Physical Requirements
Frequently perform desk-based computer tasks, seated work, and use light/fine grasping
Occasionally stand, walk, and write by hand; lift, carry, push/pull objects weighing up to 10 pounds
* Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform essential functions of the job.
Working Conditions May work extended or non-standard hours based on project or business cycle needs.
Compensation & Benefits The expected pay range for this position is $112,292 to $132,108 per annum. Stanford University pay ranges reflect good faith estimates. The final offer will depend on factors such as scope, responsibilities, qualifications, departmental budget, internal equity, geographic location, and external market pay.
At Stanford University, base pay is only one part of the rewards package. Detailed benefits information is available at the Cardinal at Work website: https://cardinalatwork.stanford.edu/benefits-rewards. Specifics about the rewards package may be discussed during the hiring process.
Disclaimers The job duties listed are typical for this classification and are not a comprehensive inventory of all duties. Duties may vary by department or program. Stanford is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.
Work Standards
Interpersonal skills: ability to work well with colleagues and external organizations
Promote culture of safety: commit to personal responsibility and safe practices
Comply with university policies and procedures
Additional Information
Schedule: Full-time
Job Code: 5522
Employee Status: Regular
Grade: I
Requisition ID: 107087
Work Arrangement: On Site
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Lab and Project Context Lab overview: DNA regulatory elements in the human genome harbor thousands of genetic risk variants for common and rare diseases and could reveal targets for therapeutics that aim to precisely tune cellular functions. The goal is to map the regulatory wiring that connects millions of elements with genes across many cell types. The Engreitz and Kundaje Labs have developed new experimental approaches and computational methods to enable this at scale, with prior work including Fulco et al. Nature Genetics 2019, Schnitzler & Kang et al. Nature 2024, Avsec et al. Nature Genetics 2021, Pampari et al. bioRxiv 2024. We invent tools combining single-cell genomics, CRISPR perturbations, and machine learning to assemble regulatory maps of the genome and uncover mechanisms of complex diseases. For more information, see https://www.engreitzlab.org and https://kundajelab.github.io/.
Project overview: Develop and apply computational models to interpret the function of noncoding variants or protein-coding genes across many human cell types. We lead collaborative projects in two NIH-funded consortia: MorPhiC (https://morphic.bio) and IGVF (https://www.igvf.org). MorPhiC characterizes gene functions through CRISPR perturbations and predictive modeling (Adli et al. Nature 2025). IGVF characterizes the impact of genomic variation on function by combining single-cell mapping, genomic perturbations, and predictive models (IGVF Consortium, Nature 2024). The role involves improving predictive models of variants, enhancers, and genes and applying them to large single-cell and CRISPR datasets to create a comprehensive catalog of regulatory wiring.
Who We Are Looking For We seek creative and passionate people at any career stage, including computational biologists, bioinformaticians, and software engineers. Candidates will train to lead and design team science computational projects that push genomic technology boundaries and reveal functions of genetic elements tied to human diseases. Our laboratories are co-located in the Department of Genetics and Biomedical Innovations Building at Stanford University. The department is a dynamic, interdisciplinary workplace offering access to cutting-edge technologies and a culture that values diversity of backgrounds and approaches.
Ideal Candidate The ideal candidate should have expertise in bioinformatics and computational biology workflows; statistical methods in data analysis with applications to high-throughput sequencing or other biological assays; fundamentals of software engineering; strong knowledge of molecular biology and genomics; fluency in Unix and standard programming/data analysis languages (Python, R, or equivalent); interest in mentoring and training other lab members in computational biology and statistics; excellent communication, organization, and time management skills; and creativity and motivation.
Responsibilities
Apply state-of-the-art machine learning models to large datasets, including single-cell and Perturb-seq datasets
Interpret model performance and results
Develop standards and pipelines to expand analyses across datasets
Interface with collaborators at Stanford and collaborating labs to design and produce methods and data analysis products
Track and manage contributions by lab members to consortium activities
Design and implement generalizable algorithms and tools for analysis of biological data, including high-throughput functional genomics assays
Evaluate and recommend new emerging technologies, approaches, and problems
Create scientifically rigorous visualizations, communications, and presentations of results
Contribute to generation of protocols, publications, and intellectual property
Maintain and organize computational infrastructure and resources
Note: Other duties may also be assigned.
Qualifications (Desired)
Required: M.S. or Ph.D. in computational biology, genetics, computer science, statistics, math, molecular biology, or related field, or equivalent practical experience. Talented applicants of all levels are encouraged to apply.
Demonstrated expertise in statistical methods in data analysis, preferably with applications to high-throughput sequencing or other biological assays
Experience with data analysis and management, workflow management
Fluency in Unix, standard bioinformatics tools (Python, R, or equivalent), and a programming language (C/C++, Java)
Strong knowledge of molecular biology and functional genomics
Ability to mentor and train other lab members in computational biology and statistics
Excellent communication, organization, and time management skills
Creative, organized, motivated, team player
A passion for science and a sense of urgency to find new medicines to benefit patients
Education & Experience (Required) Master's degree in biostatistics, statistics, or related field and at least 3 years of experience.
Knowledge, Skills and Abilities (Required)
Proficient in at least two of R, SAS, SPSS, or STATA
Skills in descriptive analysis, modeling of data, and graphic interfaces
Outstanding ability to communicate technical information to both technical and non-technical audiences
Demonstrated excellence in at least one area of expertise (e.g., coordinating studies; statistical methodology such as missing data, survival analysis, statistical genetics, or informatics; statistical computing; database design; graphical techniques)
Certifications & Licenses None
Physical Requirements
Frequently perform desk-based computer tasks, seated work, and use light/fine grasping
Occasionally stand, walk, and write by hand; lift, carry, push/pull objects weighing up to 10 pounds
* Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform essential functions of the job.
Working Conditions May work extended or non-standard hours based on project or business cycle needs.
Compensation & Benefits The expected pay range for this position is $112,292 to $132,108 per annum. Stanford University pay ranges reflect good faith estimates. The final offer will depend on factors such as scope, responsibilities, qualifications, departmental budget, internal equity, geographic location, and external market pay.
At Stanford University, base pay is only one part of the rewards package. Detailed benefits information is available at the Cardinal at Work website: https://cardinalatwork.stanford.edu/benefits-rewards. Specifics about the rewards package may be discussed during the hiring process.
Disclaimers The job duties listed are typical for this classification and are not a comprehensive inventory of all duties. Duties may vary by department or program. Stanford is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.
Work Standards
Interpersonal skills: ability to work well with colleagues and external organizations
Promote culture of safety: commit to personal responsibility and safe practices
Comply with university policies and procedures
Additional Information
Schedule: Full-time
Job Code: 5522
Employee Status: Regular
Grade: I
Requisition ID: 107087
Work Arrangement: On Site
#J-18808-Ljbffr